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In view of the different views of academia on the weight allocation of vulnerability assessment indicators, this study creatively proposed a data-based objective evaluation framework of water resource vulnerability, and applied it to the evaluation of water resource vulnerability in Hubei Province. According to the conceptual model of DPSIR proposed by the United Nations, five vulnerability factors are proposed: driving force, pressure, state, influence and response. In this study, 15 indicators were selected and the projection tracing model was used to identify vulnerability. Aiming at the complex problem of optimization calculation of projection index function in the projection tracing model, the accelerated genetic algorithm is used to speed up the optimization speed, solves the optimization problem in the process of projection tracing, and determines the objective weight of all indicators. Example calculation shows that the model can deal with complex multi-index optimization problems, and is an effective way to solve the comprehensive evaluation of complex vulnerability, and the weighting method is important for the evaluation of water resources vulnerability. The results of this paper show that the combination of projection tracing method and machine learning algorithm can improve the efficiency, objectivity and accuracy of high-dimensional data analysis, and can provide scientific basis for policy makers. [ABSTRACT FROM AUTHOR]

Integrating the Internet of Things (IoT) into urban infrastructure has reached critical mass as the movement toward Smart City (SC) development has gained momentum. Although the widespread use of IoT technology has led to more efficient and sustainable urban settings, the proliferation of linked devices and sensors has prompted legitimate safety concerns. Hence, cybersecurity has emerged as a significant obstacle to implementing SC technologies. The complexity of the SC environment necessitates additional security measures beyond the usual fare of firewalls and intrusion detection systems. To detect possible cybersecurity risks and vulnerabilities, this article presents a Challenge and Vulnerability Assessment Method (CVAT) for an IoT-enabled SC environment. The suggested method employs Internet of Things (IoT) sensors and gadgets to keep an eye on the SC's ecosystem and spot any dangers that may arise. This research creates a simulation environment to show how well the suggested strategy works. The results of the sample tests demonstrate the effectiveness of the proposed CVAT in detecting security flaws and suggesting fixes. The proposed approach has several benefits over current processes, such as detecting real-time vulnerabilities, scaling, and adapting to various SC applications. Cybersecurity threats in IoT-enabled SC environments can be mitigated with the help of the proposed CVAT method. [ABSTRACT FROM AUTHOR]

Lahore is among the most important historical cities in Pakistan, and many cultural heritage structures, such as Shalamar Gardens and Lahore Fort, which are recognised as World Heritage Sites by UNESCO, are located there. The rapid increase in transit operations and infrastructure development in Lahore can increase the vulnerability of nearby heritage structures to ground-borne vibrations. In this study, a vibration vulnerability assessment of the heritage structures present in Lahore was performed with the help of the framework provided by the Federal Transit Administration (FTA), USA. A remote survey was carried out to gather valuable information regarding the distance between the heritage structures and potential vibrational sources, as well as the latitude and longitude of the heritage structures, via the web-based mapping tool Google Earth. A vibration vulnerability map of heritage structures was developed on the basis of a vibration vulnerability assessment to make it convenient for policymakers to visualise the present state of heritage structures with respect to vibration vulnerability. The performed vibration vulnerability assessment was validated by experimentally measuring vibrations at the Buddha's Tomb (Buddha Ka Awa) heritage structure. The study revealed that 17% of the heritage structures present in Lahore are vulnerable to vibrations at present, with potential vibrational sources such as road traffic, metro trains, and rail traffic. The vibration vulnerability assessment revealed that 72% of the vibrationally vulnerable heritage structures in Lahore are most influenced by vibrations from road traffic. The results of this study can aid policymakers in creating intervention packages for vibrationally vulnerable heritage structures in Lahore in relation to their respective needs, which will make it possible to achieve the sustainable conservation of heritage structures. [ABSTRACT FROM AUTHOR]

To address impacts of climate change and other stressors on stream ecosystems, managers must prioritize resources and locations for conservation actions to facilitate effective cross‐boundary solutions. Through a science management partnership, we co‐produced a spatially explicit landscape assessment framework for cold‐water fish habitat and riparian corridors in three large watersheds of the southwestern United States. Using literature review and stakeholder workshops, we developed indicators of vulnerability and built spatial datasets depicting areas of low to high vulnerability based on exposure, sensitivity, and adaptive capacity for each resource. We found that, in general, vulnerability was greater for cold‐water fish habitat than for riparian corridors. The Little Colorado River and San Juan River watersheds had the highest percentages of subwatersheds with high vulnerability of cold‐water fish habitat. Conversely, the Upper Rio Grande watershed had the highest percentage with high vulnerability of riparian corridors. Assessments like ours facilitate the management of water resources at the scale of watersheds or river basins and incorporate physical characteristics, land‐use history, current management practices, and status of imperiled species into actionable management plans. [ABSTRACT FROM AUTHOR]

Current diagnostic technique in direct identification of multi‐site plaques and simultaneous assessment of plaque vulnerability remains a challenge, which is crucial for indicating the risk of atherosclerotic cardiovascular diseases (ASCVD). Herein, an osteopontin (OPN)‐specific nanoprobe (OPN Ab‐Au/FeNiPO4@ICG) with both multiple spectra optoacoustic tomography (MSOT) and computed tomography (CT) imaging, is constructed successfully realizing systemic screening of vulnerable plaque. OPN Ab‐Au/FeNiPO4@ICG nanoprobe specifically targeted OPN‐overexpressed foam cells and recognized the vulnerable plaque at the molecular level. In AS mice, CT imaging exhibits that OPN Ab‐Au/FeNiPO4@ICG nanoprobe effectively avoid interference from calcification and accurately visualized AS plaque. MSOT functional imaging results reveals that after the injection of OPN Ab‐Au/FeNiPO4@ICG nanoprobe, the carotid plaque exhibited a much higher MSOT signal than the aortic arch plaque (P = 0.0291). Further pathological analysis displays that the carotid plaque possessed a much higher vulnerability score (P = 0.0247), in agreement with the MSOT signals. More importantly, the linear regression analysis confirms the high correlation between the MSOT signals and plaque vulnerability with R = 0.7095 (P = 0.0216), demonstrating the potential of the proposed nanoprobe in systematic evaluation of plaque vulnerability. This work employs the dual‐model nanoprobe strategy for both plaque localization and vulnerability assessment, greatly advancing the accurate diagnosis of ASCVD. [ABSTRACT FROM AUTHOR]

Tsunamis, generated by submarine earthquakes, landslides, or volcanic eruptions, are a significant hazard to coastal areas owing to their sudden onset and rapid propagation speed. Thus, tsunami disaster risk assessment is crucial to determine potential losses and severity of future tsunami impacts, providing essential support for disaster prevention and mitigation efforts. This study presents a case study of Xiamen City in Fujian province for developing a high-precision tsunami model. Utilizing nearshore water depth and digital elevation model data, we established a high-resolution tsunami numerical simulation model for Xiamen City that accounts for the impact of water overrunning levees and overshoals. Historical tsunami disaster records were used to validate the numerical model. By determining multiple tsunami source scenarios that could potentially affect the counties of Xiamen City, we simulated the inundation range and water depth distribution required for the potential maximum tsunami event. The simulation results facilitated a tsunami hazard assessment. Considering land use and important tsunami-affected exposures including industrial and chemical enterprises and ports, we evaluated the vulnerability of Xiamen City to tsunami disasters. Based on the determination of hazard and vulnerability levels, we investigated the risk distribution of tsunami disasters in Xiamen City. The results of this study lay the groundwork for developing methodologies to improve tsunami disaster risk assessment in coastal areas. [ABSTRACT FROM AUTHOR]

Copyright of Fire Ecology is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The south-western coastal area of Bangladesh proximity to Sundarbans is a hot spot of climate change. The perception and problems along with associated risks to the adaptation of climate change are not well understood. However, the present study was conducted in 2020–2021 through a multistage sampling technique, questionnaire survey, and focus group discussions (FGD). In addition, the Likert scale and the INFORM model are used to understand perceptions, problems, and extent of risks response to climate change adaptation in the study area. This study illustrates that early-aged illiterate, medium families (5–7 in numbers) and $ 18.45–$ 36.88 per month income level are more susceptible to the adaptation of climate change. The majority of the respondents are dependent on natural resources, such as rainwater (67.3%), dried leaves (65.4%, and fishing from Sundarbans (80.6%). Besides, most of the respondents experienced disasters (66.7%), water logging and salinity (65.05%), waterborne diseases (89.5%), and monetary problems (81%), which forced them to stay in a more critical lifestyle. The perception level was high in the unconcerned level (77.15%) followed by concerned (11.4%) and very concerned (9.5%), while only 1.95% showed neutral and none of them were very concerned. Likewise, in the study region, respondents are confronted with a variety of risks and exposure (42%), vulnerabilities (33%), and lack of capacity (25%). Also, significant differences in perception level and no significant differences were observed in risk level in this study. These findings insights into the necessity of educational and financial interventions to optimize identified risks and problems and increase the perception level of effective policy-making processes and adaptation strategies to the adaptation of climate change. [ABSTRACT FROM AUTHOR]

The present study conducts a comparative analysis of India's eight Tier-1 cities, Mumbai, Delhi, Chennai, Kolkata, Bengaluru, Hyderabad, Ahmedabad, and Pune, for their climate vulnerability assessment. The secondary data was collected from the India Meteorological Department and published climate vulnerability atlas to draw the relative importance index (RII) score for each exposure indicator of climate vulnerability for each city on the ten climate parameters, including earthquake, rainfall, wind, thunderstorm, cyclone, drought, floods, heatwave, cold wave vulnerabilities. The cities based on inland and coastal boundaries were also considered in scoring calculations. The study's findings indicate that Chennai and Mumbai are the most vulnerable cities, primarily due to extreme weather events and sea-level rise, necessitating targeted adaptation strategies. Bengaluru shows lower vulnerability, reflecting its resilient infrastructure and proactive measures. Mid-range vulnerabilities are observed for Delhi, Kolkata, Ahmedabad, and Pune, highlighting diverse challenges from heat waves to flooding. Hyderabad's relatively lower score suggests moderate risk yet underscores the importance of continuous infrastructure improvement. The comparative analysis offers critical insights for urban planners and policymakers to develop tailored climate resilience strategies, emphasising sustainable infrastructure, community preparedness, and proactive urban design to mitigate climate risks and foster sustainable development across these urban conglomerates. The study serves as a foundational step towards enhancing the climate resilience of India's major cities. [ABSTRACT FROM AUTHOR]

The risk assessment of the area, with emphasis on the transport infrastructure, is dealt with in this paper. Disruption of some elements of transport infrastructure can lead to major negative impacts on the functioning of society, economies or states. The article therefore starts with presentation of the characteristics and importance of the road transport infrastructure with regard to its vulnerability. The core of the paper is the identification of vulnerable elements of road transport infrastructure and their parts and the subsequent vulnerability assessment of the selected element of the road transport infrastructure in the selected area. Therefore, the aim of the research, presented in this paper, was to propose a method for identifying and assessing the vulnerability of road transport infrastructure elements using the latest approaches in the field of vulnerability assessment. The proposed vulnerability assessment procedure is applied in the conditions of the city of Rajec in Slovakia. [ABSTRACT FROM AUTHOR]

This study proposes a multidimensional framework for the assessment of cultural heritage vulnerability to flood hazards. In this framework, cultural heritage is framed within a landscape approach drawing on the concept of the historic urban landscape as a physical entity, a lived space and a layered socio-economic environment. In such manner, this study emphasises that climate change disturbs both tangible and intangible aspects of cultural heritage – such as social systems and daily life practices – by modifying the material conditions that sustain them. Thus, the proposed framework integrates built and natural heritage with area-based vulnerability and socio-economic deprivation, considering households' exposure to flood risk. The analysis follows a spatial and quantitative approach using open geospatial data available at the statistical unit level. The data are processed and analysed employing QGIS software and statistical analysis. Results confirm that cultural heritage vulnerability is highly associated with socio-economic deprivation and area-based vulnerability. The understanding of this interrelation is key to the identification of historic urban landscape qualities and priority areas for reconstruction and social support. [ABSTRACT FROM AUTHOR]

We introduce a new property called robust reachability which refines the standard notion of reachability in order to take replicability into account. A bug is robustly reachable if a controlled input can make it so the bug is reached whatever the value of uncontrolled input. Robust reachability is better suited than standard reachability in many realistic situations related to security (e.g., criticality assessment or bug prioritization) or software engineering (e.g., replicable test suites and flakiness). We propose a formal treatment of the concept, and we revisit existing symbolic bug finding methods through this new lens. Remarkably, robust reachability allows differentiating bounded model checking from symbolic execution while they have the same deductive power in the standard case. Finally, we propose the first symbolic verifier dedicated to robust reachability: we use it for criticality assessment of 5 existing vulnerabilities, and compare it with standard symbolic execution. [ABSTRACT FROM AUTHOR]

The vulnerability assessment of buildings in future scenarios is critical to decrease potential losses caused by debris flows in mountainous areas due to the complex topographical condition that could increase the environmental vulnerability to climate change. However, the lack of reliable methods limits the accurate estimation of physical damage and the associated economic loss. Therefore, an integrated method of physical vulnerability matrix and machine learning model was developed to benefit the estimation of damage degree of buildings caused by a future debris-flow event. By considering the building structures (reinforced-concrete (RC) frame and non-RC frame), spatial positions between buildings and the debris-flow channels (horizontal distance (HD) and vertical distance (VD)), and impact pressure (P t) to buildings, a physical vulnerability matrix was proposed to link physical damage with the four factors. In order to overcome the difficulty in estimating the possible impact pressure to buildings, an ensemble machine learning (ML) model (XGBoost) was developed with the involvement of geological factors. Additionally, the HD and VD were decided based on the satellite images. The Longxihe Basin, Sichuan, China was selected as a case study. The results show that the ML model can achieve a reliable impact pressure prediction because the mean absolute percentage error (MAPE), root mean squared error (RMSE), and mean absolute error (MAE) values are 9.53 %, 3.78 kPa, and 2.47 kPa. Furthermore, 13.9 % of buildings in the Longxihe Basin may suffer severe damage caused by a future debris-flow event, and the highest economic loss is found in a residential building, reaching 5.1×105 €. Overall, our work can provide scientific support for the site selection of future constructions. [ABSTRACT FROM AUTHOR]

With the increase in the proportion of photovoltaic and wind power access, the scale and form of distribution networks are becoming more and more complex. The traditional single distribution network vulnerability assessment method is difficult to use to identify the vulnerable links in the distribution network. Therefore, this paper proposes a method for identifying and evaluating vulnerable links in distribution networks based on minimum discriminant information. First, considering the influence of distributed grid connection, an improved probabilistic power flow calculation method is proposed, which improves the calculation efficiency and accuracy. Second, considering the correlation degree, transmission capacity, and voltage stability of branches in the distribution network, the identification index of vulnerable lines is defined. Based on power quality and operating state, the identification index of vulnerable nodes in a distribution network is defined. Finally, based on the indicators of vulnerable nodes and vulnerable lines, the vulnerable links in the distribution network are comprehensively evaluated based on the principle of minimum discriminant information, and the vulnerable links of the entire distribution network are evaluated according to different degrees of vulnerability. The rationality and effectiveness of the proposed method are verified via an example analysis of actual power grid data. [ABSTRACT FROM AUTHOR]

Wireless local area networks (WLANs) play a crucial role in the internet of things (IoT) landscape, facilitating constant data exchange among devices. However, the inherent security vulnerabilities in these networks, stemming from limited computational resources, pose significant challenges to deploying robust security measures. This research addresses the security concerns surrounding IoT devices within the IEEE 802.11ah WLAN environment by introducing the SecureScanML algorithm, a novel machine learning (ML) approach designed to optimize Internet-wide port scans (IWPS) for enhanced device security while preserving network performance. The SecureScanML algorithm leverages Q-learning, a reinforcement learning technique, to dynamically adjust the scan rates of IoT devices adaptively. Through this approach, the algorithm effectively reduces vulnerabilities, achieving a notable 35.7% reduction, while maintaining key network performance metrics. With a throughput of 2.8 Mbps, a packet delivery ratio of 97.3%, an adaptability index of 0.91, a convergence speed of 420 s, and a low latency of 42 ms, the proposed algorithm surpasses existing methods such as TA, RA, SSR, CNN-LSTM, RLA, and RSR. Simulation results corroborate the efficacy of SecureScanML in mitigating vulnerabilities without compromising network efficiency. The algorithm strikes a fine balance between proactive vulnerability management and network performance preservation. Moreover, the study explores the impact of security-performance weight parameters on the algorithm's behavior, providing valuable insights for fine-tuning the system to achieve specific security and performance objectives. Notably, for a security-performance weight of 0.5, the algorithm demonstrates high throughput and vulnerability reduction alongside low latency. [ABSTRACT FROM AUTHOR]

The United Nations Sustainable Development Goals (SDGs) unanimously agreed upon the successful implementation of SDG 14, which aims to preserve and sustainably utilize oceans, seas, and marine resources for sustainable development. This necessitates collaborative efforts on a global scale to protect, conserve, restore, and sustainably manage coastal ecosystems. In order to successfully implement this SDG, communities in coastal countries need to sustainably foster their response capacities and preparedness in the face of coastal multi-hazards. This can be achieved by establishing guidelines to mitigate, prepare for, and respond to tsunamis as well as by working with those communities to help them meet these guidelines and ultimately become ʻTsunami Readyʼ. This can be realized by raising awareness and increasing resilience amongst those communities and by devising preparedness strategies as required by Ocean Decade Challenge 6. This study investigates tsunami resilience and evacuation strategies along Alexandria's coastlines. It specifically focuses on the Bibliotheca Alexandrina's area, which is a site of immense historical, educational and residential significance. In this study, we leverage the results of tsunami modeling, coupled with advanced remote sensing and GIS techniques to identify risk zones, and design evacuation plans highlighting critical areas that require immediate attention during disaster preparedness. The study also underscores the importance of integrating scientific methods into urban planning to safeguard lives, heritage and vital infrastructure. [ABSTRACT FROM AUTHOR]

Flood vulnerability assessment (FVA) informs the disaster risk reduction and preparedness process in both rural and urban areas. However, many flood-vulnerable regions like Malawi still lack FVA supporting frameworks in all phases (pre-trans-post disaster). Partly, this is attributed to lack of the evidence-based studies to inform the processes. This study was therefore aimed at assessing households' flood vulnerability (HFV) in rural and urban informal areas of Malawi, using case studies of Traditional Authority (T/A) Kilupula of Karonga District (KD) and Mtandire Ward in Lilongwe City (LC). A household survey was used to collect data from a sample of 545 household participants. Vulnerability was explored through a combination of underlying vulnerability factors (UVFs)-physical-social-economic-environmental and cultural with vulnerability components (VCs)-exposure-susceptibility and resilience. The UVFs and VCs were agglomerated using binomial multiple logit regression model. Variance inflation factor (VIF) was used to check the multicollinearity of variables in the regression model. HFV was determined based on the flood vulnerability index (FVI). The data were analysed using Multiple Correspondence Analysis (MCA), artificial neural network (ANN) and STATA. The results reveal a total average score of high vulnerability (0.62) and moderate vulnerability (0.52) on MCA in T/A Kilupula of Karonga District and Mtandire Ward of Lilongwe City respectively. The FVI revealed very high vulnerability on enviroexposure factors (EEFs) ( 0.9 ) in LC and (0.8 ) in KD, followed by ecoresilience factors (ERFs) (0.8) in KD and (0.6 ) in LC and physioexposure factors (PEFs) ( 0.5) in LC besides 0.6 in KD for the combined UVFs and VCs. The study concludes that the determinants of households' flood vulnerability are place settlement, low-risk knowledge, communication accessibility, lack of early warning systems, and limited access to income of household heads. The study recommends that an FVA framework should be applied to strengthen the political, legal, social, and economic responsibilities of government for building the resilience of communities and supporting planning and decision-making processes in flood risk management. [ABSTRACT FROM AUTHOR]

Frequency-Area Distribution (FAD) analyses were introduced to landslides research since the early 2000's. This technique is a powerful tool that allows assessing the completeness of landslide inventory maps (LIM), used to build both landslides susceptibility and landslides hazard assessment models. However, FAD analyses are not commonly used in such studies despite the significant potential of the technique. The long processing steps needed to generate FAD curves, which involve logarithmic binning and iterative model fitting using various statistical tools, constitutes an energy and time-consuming task that pushes many researchers away from using the technique. In fact, no fully automatic tool capable of generating FAD curves and models exists as of July 2023. Therefore, we attempt to provide a fully automatic computer program capable of binning, fitting FAD curves and assessing their goodness of fit to theoretical models in a fully automatic, one step process. An example is provided using real data from Taounate province, Northern Morocco, so as to demonstrate the ability of the tool to deal with exhaustive datasets. In addition, the Kolmogorov-Smirnov, goodness of fit test is added to provide an objective assessment of the data fitting, which constitutes a better alternative to the subjective visual assessment that most landslides researchers rely on. To sum up, we believe that this tool will help popularize the FAD technique, which will consequently improve the accuracy and objectivity of landslides risk and hazard assessment disciplines. [ABSTRACT FROM AUTHOR]

This paper explores the application of The World Avatar (TWA) dynamic knowledge graph to connect isolated data and assess the impact of rising sea levels in Singapore. Current sea level rise vulnerability assessment tools are often regional, narrow in scope (e.g., economic or cultural aspects only), and are inadequate in representing complex non-geospatial data consistently. We apply TWA to conduct a multi-perspective impact assessment of sea level rise in Singapore, evaluating vulnerable buildings, road networks, land plots, cultural sites, and populations. We introduce OntoSeaLevel, an ontology to describe sea level rise scenarios, and its impact on broader elements defined in other ontologies such as buildings (OntoBuiltEnv ontology), road networks (OpenStreetMap ontology), and land plots (Ontoplot and Ontozoning ontology). We deploy computational agents to synthesise data from government, industry, and other publicly accessible sources, enriching buildings with metadata such as property usage, estimated construction cost, number of floors, and gross floor area. An agent is applied to identify and instantiate the impacted sites using OntoSeaLevel. These sites include vulnerable buildings, land plots, cultural sites, and populations at risk. We showcase these sea level rise vulnerable elements in a unified visualisation, demonstrating TWA's potential as a planning tool against sea level rise through vulnerability assessment, resource allocation, and integrated spatial planning. [ABSTRACT FROM AUTHOR]

The Indian west coast is under constant threat from climate change-induced hazards. Various social, economic, and infrastructural disparities along the coast cause significant variations in climate vulnerability. Current literature assesses vulnerability either over (1) a large area with poor spatial resolution or (2) a local area with better spatial resolution. The former assessments provide more comprehensive and broad insights into large spatial trends of vulnerability, while the latter provide more accurate and specific inputs needed by the local governments for effective intervention. However, there is a lack of studies that assess vulnerability simultaneously at a high-resolution and over a large geographic area, due to inadequacies in existing methodologies and difficulty in data management and analysis. This is a key gap that we address in our paper. We assess climate vulnerability of the entire Indian west coast at the village level, and propose a novel machine-learning based methodology tailored for high-resolution assessment over large geographic areas. This helped us produce the first high-resolution (i.e. village-level) climate vulnerability map of the entire Indian west coast. We found that the state of Maharashtra has the highest number of vulnerable villages and the state of Kerala has the least number of vulnerable villages. We collate and utilize a large dataset of 112 indicators describing socioeconomic characteristics, infrastructure and availability of financial services, among other aspects, to obtain a comprehensive picture of vulnerability. We analyze geospatial trends and attribute high vulnerability to specific indicators, which will help in effective decision-making at the village level. [ABSTRACT FROM AUTHOR]

This study investigates the impacts of the COVID-19 pandemic on urban planning to develop an urban resilience strategy. The vulnerability assessment of Algiers to COVID-19 seeks to develop a correlation between the pandemics' global systemic impacts and Algiers' local urban potential. This research outlines the methodological approach of the Group Analysis Method (GAM), the multi-criteria decision analysis method (MCDA), and the qualitative analytical reading method (SFPO) - Successes, Failures, Potential, and Obstacles. These methods allowed us to build the Restricted Targeted Self-Audit (RTSA) and finally the planning framework. This study yields two distinct types of results: theoretical findings related to the expansion of the urban sustainability model and the empirical findings triggered by the issue of pandemic resilience. The results of this study aim to enhance residents' adaptation to major urban risks through proactive urban planning actions. This will help prevent a critical crisis for the city and its inhabitants in the event of a future pandemic. [ABSTRACT FROM AUTHOR]

Copyright of Urban Geology is the property of Urban Geology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract In view of the different views of academia on the weight allocation of vulnerability assessment indicators, this study creatively proposed a data-based objective evaluation framework of water resource vulnerability, and applied it to the evaluation of water resource vulnerability in Hubei Province. According to the conceptual model of DPSIR proposed by the United Nations, five vulnerability factors are proposed: driving force, pressure, state, influence and response. In this study, 15 indicators were selected and the projection tracing model was used to identify vulnerability. Aiming at the complex problem of optimization calculation of projection index function in the projection tracing model, the accelerated genetic algorithm is used to speed up the optimization speed, solves the optimization problem in the process of projection tracing, and determines the objective weight of all indicators. Example calculation shows that the model can deal with complex multi-index optimization problems, and is an effective way to solve the comprehensive evaluation of complex vulnerability, and the weighting method is important for the evaluation of water resources vulnerability. The results of this paper show that the combination of projection tracing method and machine learning algorithm can improve the efficiency, objectivity and accuracy of high-dimensional data analysis, and can provide scientific basis for policy makers.

Abstract Lahore is among the most important historical cities in Pakistan, and many cultural heritage structures, such as Shalamar Gardens and Lahore Fort, which are recognised as World Heritage Sites by UNESCO, are located there. The rapid increase in transit operations and infrastructure development in Lahore can increase the vulnerability of nearby heritage structures to ground-borne vibrations. In this study, a vibration vulnerability assessment of the heritage structures present in Lahore was performed with the help of the framework provided by the Federal Transit Administration (FTA), USA. A remote survey was carried out to gather valuable information regarding the distance between the heritage structures and potential vibrational sources, as well as the latitude and longitude of the heritage structures, via the web-based mapping tool Google Earth. A vibration vulnerability map of heritage structures was developed on the basis of a vibration vulnerability assessment to make it convenient for policymakers to visualise the present state of heritage structures with respect to vibration vulnerability. The performed vibration vulnerability assessment was validated by experimentally measuring vibrations at the Buddha’s Tomb (Buddha Ka Awa) heritage structure. The study revealed that 17% of the heritage structures present in Lahore are vulnerable to vibrations at present, with potential vibrational sources such as road traffic, metro trains, and rail traffic. The vibration vulnerability assessment revealed that 72% of the vibrationally vulnerable heritage structures in Lahore are most influenced by vibrations from road traffic. The results of this study can aid policymakers in creating intervention packages for vibrationally vulnerable heritage structures in Lahore in relation to their respective needs, which will make it possible to achieve the sustainable conservation of heritage structures.

Abstract The south-western coastal area of Bangladesh proximity to Sundarbans is a hot spot of climate change. The perception and problems along with associated risks to the adaptation of climate change are not well understood. However, the present study was conducted in 2020–2021 through a multistage sampling technique, questionnaire survey, and focus group discussions (FGD). In addition, the Likert scale and the INFORM model are used to understand perceptions, problems, and extent of risks response to climate change adaptation in the study area. This study illustrates that early-aged illiterate, medium families (5–7 in numbers) and $ 18.45–$ 36.88 per month income level are more susceptible to the adaptation of climate change. The majority of the respondents are dependent on natural resources, such as rainwater (67.3%), dried leaves (65.4%, and fishing from Sundarbans (80.6%). Besides, most of the respondents experienced disasters (66.7%), water logging and salinity (65.05%), waterborne diseases (89.5%), and monetary problems (81%), which forced them to stay in a more critical lifestyle. The perception level was high in the unconcerned level (77.15%) followed by concerned (11.4%) and very concerned (9.5%), while only 1.95% showed neutral and none of them were very concerned. Likewise, in the study region, respondents are confronted with a variety of risks and exposure (42%), vulnerabilities (33%), and lack of capacity (25%). Also, significant differences in perception level and no significant differences were observed in risk level in this study. These findings insights into the necessity of educational and financial interventions to optimize identified risks and problems and increase the perception level of effective policy-making processes and adaptation strategies to the adaptation of climate change.

With the sheer amount of vulnerabilities, manually evaluating the impact of them is challenging. This paper proposes employing artificial intelligence models as substitutes for humans or as aides to human experts in estimating vulnerabilities. We compare the precision, recall, and F1 score amongst the Universal Sentence Encoder, Generative Pre-trained Transformer, and Support Vector Machine, trained on 118,000 vulnerabilities and tested on 51,000 vulnerabilities, with human experts on mean estimation error and variance for each type of vulnerability from the state of the art work in estimating vulnerability severity scores. The Universal Sentence Encoder demonstrates superior performance with results (72/77 % accuracy on severity level prediction) that significantly outperform human experts in assessment tasks for various types of vulnerabilities with high efficiency for memory consumption and low running time. Additionally, we examine the efficacy of our models in predicting the components of vulnerability evaluation metrics and the severity level of vulnerabilities. The findings highlight the potential of artificial intelligence agents to assist cybersecurity experts in this task which in the current state of the art is entirely manual. [ABSTRACT FROM AUTHOR]

The holistic risk assessment model defines vulnerabilities that may vary depending on the type of hazard in the urban settlement. This model aims to identify the priority issues that need improving across the entire city and the spatial distribution of these issues. The paper describes the physical risk assessment phase of the model. The model emphasizes evaluating natural events such as earthquakes, floods, landslides, and climatic events together. The pairwise comparison method through the literature review has defined the criteria and weights selected for the model, and the susceptibility, vulnerability, and risk mapping are made using a weighted sum model through geographic information systems. Risk size is assigned a numeric value from 1 to 5, with 1 being the lowest. In conclusion, the paper provides physical risk distribution mapping of Kırklareli Central district, the study area, based on each hazard type. The paper also defines the multi-hazard risk, combining maps with the weighted sum. Accordingly, the paper has demonstrated that approximately 80% of the central district settlement is a medium-risk area, and 2.85% is a high-risk area. [ABSTRACT FROM AUTHOR]

Cybersecurity threats are among the most significant risks facing organizations and government today, and administrative boards have now been held accountable. This is an experimental research activity conducted to perform a holistic cybersecurity assessment and vulnerability modelling on the Information and Communication Technology (ICT) infrastructure and services of Colleges of Education in the six geopolitical zones. The study adopts an integrated bi-modal threat modelling and assessment (IBTMA) method by combining assessment and modelling approaches, which involves mixed-methods, along with computer-based experimentation to comprehensively evaluate and model cybersecurity threats, identify vulnerabilities, and propose effective mitigation strategies. Logistic regression data analysis was used to model the relationship between dependent variables (e.g., presence or absence of vulnerabilities or threats) and independent variables (e.g., cybersecurity practices, system configurations, policies, and staff training programs). This cybersecurity assessment provides the initial understanding of the security landscape and practices. The next step involves using the Microsoft Threat Modeling tool on the assets to identify specific threats. These threats are then prioritized based on their potential impact and likelihood. Assessment result of the vulnerability exposure is supported by the threat modelling report, which shows several threats: tampering, elevation of privilege, denial of service, privilege escalation, information disclosure, and spoofing. Findings from the study indicate that colleges face critical network and web vulnerabilities that need holistic solution.

Assessing the vulnerability of the economy, society, and environment of the belt and road initiative (BRI) countries and identifying the distribution characteristics are significant to promoting the construction of the Belt and Road. Based on the panel data of the BRI countries, this study selects 22 indicators from three aspects of the economy, society, and environment to construct a comprehensive indicator evaluation system and propose a multi-index and long time series vulnerability calculation scheme. Furthermore, the spatial differentiation characteristics and influencing factors of vulnerability in BRI countries are studied. The results show hierarchical differences in the comprehensive vulnerability index of BRI countries, and the low-vulnerability countries accounted for the largest proportion. The economic vulnerability, social vulnerability, and environmental vulnerability of the BRI countries all have apparent spatial variations. Among them, the vulnerability of various systems in Central and Eastern Europe and Northeast Asia is relatively low, while most of West Asia, northern South Asia, southern Central Asia, and some parts of Southeast Asia have relatively high vulnerability. In addition, economic development, economic structure, resource security, and infrastructure are all factors that have a greater impact on the comprehensive vulnerability, while the impact intensity varies with regional differences. Finally, this paper puts forward appropriate suggestions. With the goal of reducing the comprehensive vulnerability, this paper tries to provide a reference for better promoting the sustainable development of the BRI. [ABSTRACT FROM AUTHOR]

Comprehensive information on vulnerability patterns is critical for effectively integrating response actions for climate change adaptation and mitigation at the district level. Unfortunately, in developing countries, a large number of local authorities and policymakers are lacking in the requisite information. Moreover, there needs to be more uniformity in the methodology employed for vulnerability analysis at the district level, and incorporating broader assessments at a district level is vital. The spatial evaluation of vulnerability patterns to climate change in 166 districts in the central region of India reveals areas that require immediate attention for adaptation action. The study facilitates a framework for the evaluation of the vulnerability of districts to climate change based on three components (exposure, sensitivity, and adaptive capacity) accountable for over 83.5% of the overall data set's cumulative variability. Spatial multicriteria evaluation (SMCE) combines vulnerability components and indicators into a single framework. SMCE is used to develop a criteria tree, normalize indicators, and allocate weights to all elements, facets, and indicators. The study illustrated that high vulnerability was reported in the Jaipur, Raipur, Durg, West-Nimar, Jalgaon, Nashik, Faizabad, and Jaunpur Districts, covering an estimated 39.76% of the entire region. Additionally, roughly 26.50% of the area was determined to possess a significant level of vulnerability. It was observed that 60% of the population falls into moderate to high vulnerability categories in the central region. The study introduces a decision-making process related to climate change and vulnerability that assists policymakers in formulating strategies for climate mitigation and adaptation measures focusing on sustainability. [ABSTRACT FROM AUTHOR]

Coastal areas are among the planet's most dynamic yet vulnerable environments, the shape of sand dunes being typically modeled by two sets of forces: the relatively constant action of wind and waves and the disturbances caused by infrequent but powerful storms. These meteorological events affect the plant communities, but can also play an important role in the long-distance dispersal of some psammophytes. Plants can be exposed, uprooted, broke apart and spread by wind and waves. Then, depending on the resilience of the plant to seawater temperature, the duration of immersion, the type of fragment transported and the suitability of the new habitat, new population can be established. This study aimed to assess the resilience to seawater immersion and the ability of long-distance dispersal of three psammophytes: Convolvulus persicus, Alyssum borzaeanum, and Silene thymifolia. The experiments tested the viability of vegetative fragments and the reproductive capacity of seeds under different exposure periods (5, 10, and 15 days) and temperature conditions: 4 °C (average surface seawater temperature during winter and early spring when storms on the Black Sea Coast occur), 13 °C (average surface seawater temperature), and 23 °C (average summer surface seawater temperature). The increase of both seawater temperature and the exposure time had a significant negative influence on the viability of the vegetative fragments and on germination capacity. After exposure to seawater, Convolvulus persicus, and Silene thymifolia are able to establish new populations through vegetative fragments or seeds, whilst in case of Alyssum borzaeanum, no vegetative regeneration was observed. [ABSTRACT FROM AUTHOR]

Flood is one of the most common and severe dangers that disrupt people's lives around the world.Khorramabad city is at risk of flooding due to its location in a valley, torrential rains, and the presence of the Karganeh and Khorramroud rivers in the city. And the purpose of this research is to assess the vulnerability of urban facilities Khorram Abad city. This research is applied in terms of purpose and descriptive-analytical in terms of method. ARC GIS software and AHP-FUZZY hybrid model were used to prepare vulnerability maps. In order to determine the criteria, experts have been consulted after that, the paired criteria matrix is formed, and then according to the matrices, a questionnaire has been prepared that compares the criteria two by two according to their importance. Finally, the questionnaires were given to 25 experts and specialists and completed. The result of scoring the criteria was entered into the special software of the AHP (Expert choice) model and the final weight of the sub-criteria was determined, and in the next step, the layers (sub-criteria) were fuzzified and then the layers were combined, and as a result. The final map of the vulnerability of the Khorramabad facilities was obtained. In this research, two natural and physical criteria, which include 12 sub-criteria: worn texture, population density, road network, use, height, slope, slope direction, geology, distance from the river, soil science, subsidence, rainfall amount Used. The results of the research showed that 9.66 percent or 49 hectares of urban facilities are very vulnerable to floods. It was also found that the use of green spaces, office use, educational use and commercial use are the most vulnerable, and historical use and cultural use are the least vulnerable to floods. In order to reduce the flood vulnerability and to increase the safety of urban facilities, it is necessary to organize and improve the quality of the bed and river boundaries and improve the physical quality of rivers under the supervision of city managers and officials. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive analysis of natural hazard susceptibility in the Makedonska Kamenica municipality of North Macedonia, encompassing erosion assessment, landslides, flash floods, and forest fire vulnerability. Employing advanced GIS and remote sensing (RS) methodologies, hazard models were meticulously developed and integrated to discern areas facing concurrent vulnerabilities. Findings unveil substantial vulnerabilities prevalent across the area, notably along steep terrain gradients, river valleys, and deforested landscapes. Erosion assessment reveals elevated rates, with a mean erosion coefficient (Z) of 0.61 and an annual erosion production of 182,712.9 m3, equivalent to a specific erosion rate of 961.6 m3/km2/year. Landslide susceptibility analysis identifies 31.8% of the municipality exhibiting a very high probability of landslides, while flash flood susceptibility models depict 3.3% of the area prone to very high flash flood potential. Forest fire susceptibility mapping emphasizes slightly less than one-third of the municipality's forested area is highly or very highly susceptible to fires. Integration of these hazard models elucidates multi-hazard zones, revealing that 11.0% of the municipality's territory faces concurrent vulnerabilities from excessive erosion, landslides, flash floods, and forest fires. These zones are predominantly located in upstream areas, valleys of river tributaries, and the estuary region. The identification of multi-hazard zones underscores the critical need for targeted preventive measures and robust land management strategies to mitigate potential disasters and safeguard both human infrastructure and natural ecosystems. Recommendations include the implementation of enhanced monitoring systems, validation methodologies, and community engagement initiatives to bolster hazard preparedness and response capabilities effectively. [ABSTRACT FROM AUTHOR]

Groundwater quality assessment has become vital issues all over the world as remediation of contaminated water is extremely challenging. Due to the unconfined nature of Nsukka and Igbo-Etiti groundwater and the highly intensive agricultural practices observed in these areas, assessment of groundwater vulnerability to contamination is inevitable. This study employed vertical electrical sounding (VES) in forty-two (42) different locations of Nsukka and Igbo-Etiti area with the intent to assess groundwater susceptibility to contamination using GOD, GODL and GLSI models. Results from VES delineated a total of four-to-five geoelectric layers. From VES result on the layer's geologic unit's constrained with the borehole log information on geologic unit serving as water-bearing unit in the area, the fourth layer was delineated as the aquifer layer in most locations of the study area within fine-to-medium coarse-grained sand. Results of groundwater vulnerability assessment as established using GODindex classified the aquifer units in the study area as low vulnerable units. GODLindex and GLSI models establishing different range of vulnerability index classified the areas aquifer units into high, moderate and low vulnerable zones. Results of single parameter and map removal sensitivity analysis on GOD and GODL model delineated overlying lithology and longitudinal conductance, respectively, as the most sensitive parameters in vulnerability assessment of the study area. Aquifer vulnerability potential zone maps generated revealed the vulnerability class distributions. These results will assist in sensitizing the habitants against indiscriminate disposal of waste and also guide the government in effective monitoring and management of groundwater repositories. [ABSTRACT FROM AUTHOR]

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Over the past few decades, the use of vulnerability assessments has grown substantially to support rural communities in developing countries. These studies aim to help these communities achieve their livelihood goals, such as sustainable resource use and adaptation to global changes, by evaluating their susceptibility to climate change impacts. This systematic review critically examines the extensive body of literature on Livelihood Vulnerability Index (LVI) assessments related to climate change impacts in developing countries. By synthesizing findings from various studies, this review highlights patterns and methodologies used to understand the effects of climate change on vulnerable populations. Key focus areas include geographical distribution, methodological approaches, and the frameworks utilized in vulnerability assessments. The review identifies prominent frameworks, such as the LVI and LVI-IPCC, which integrate indicators of sensitivity, exposure, and adaptive capacity to evaluate climate risks. Findings reveal a concentration of studies in Asia and Africa, with a strong emphasis on agricultural and coastal ecosystems. Methodologically, there is a notable reliance on stratified random sampling to accurately capture community and household-level vulnerabilities. A detailed comparative analysis of the LVI, LVI-IPCC, and Sustainable Livelihood Framework (SLF) is also presented, highlighting their characteristics, benefits, and limitations. The review underscores the need for methodological refinements to better address temporal and regional variations in vulnerability. It concludes with recommendations for future research, integrating broader climate scenarios, exploring sectoral interdependencies, and adopting dynamic approaches to enhance the accuracy and applicability of vulnerability assessments. [ABSTRACT FROM AUTHOR]

In today's highly advanced information technology environment, modern network and communication technologies are widely used in monitoring and controlling power systems. These technologies have evolved significantly. They now form a high-performance digital system known as the cyber–physical power system. However, vulnerabilities in communication networks present growing threats to these systems. This paper seeks to enhance the accurate assessment of the security posture of cyber-physical power systems by inferring attackers' intentions. A threat modeling approach based on Bayesian attack graphs is presented, employing Bayesian networks to define and evaluate potential threats that attackers could pose to different system infrastructures. The paper initially conducts a qualitative analysis of the system's threats, constructing a directed graph structure and establishing conditional probability tables among nodes based on prior knowledge. Subsequently, methods are developed to compute the threat levels at different system nodes using real-time detected attack events. Further analysis methods and security assessment metrics are also developed to identify attack paths and quantify system security. Finally, a Bayesian attack graph is constructed in accordance with the system's structure. In practical scenarios, the attack path analysis method can predict the most vulnerable attack paths, while the absolute values of the security assessment metrics indicate the overall risk level of the system. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has instigated substantial disruptions in global economic activities and food systems, prompting concerns regarding food security worldwide. However, a knowledge gap persists regarding the specific alterations in food security and the underlying factors associated to these changes. This study delves into the shifts in food security status among farm households in North-western Bangladesh in the wake of COVID-19, alongside an examination of the factors associated to these shifts. Drawing on survey data from 498 households, we employed paired sample T-tests and multinomial logit models to scrutinize variations in food consumption patterns and the Food Security Index (FSI). Our analysis unveils significant transformations in food security status, as households curtailed consumption of essential food items such as meat, fish, pulses, milk, and sugar, resulting in diminished nutrient intake. Intriguingly, while 31.9% of households exhibited food security in 2019, only 11.8% maintained food security by 2022. We identify several factors associated with changes in FSI, including the presence of migrated household members, household income, and household size. Moreover, our study sheds light on previously overlooked determinants such as the value of livestock, compliance with dietary guidelines, engagement in off-farm work, and knowledge of food elements as significant factors associated to FSI variations. In light of these findings, we underscore the imperative for targeted interventions to address food security challenges. This entails implementing agricultural projects, providing microenterprise support, and offering training programs aimed at enhancing production diversity, augmenting farmers' income, and bolstering food security. Additionally, we advocate for the implementation of social programs focusing on dietary guidelines, food nutrition education, and the facilitation of off-farm activities to alleviate food security vulnerabilities among farm households. [ABSTRACT FROM AUTHOR]

Typhoons are extremely severe weather events which seriously threaten the safety of people's lives and properties. Therefore, identifying and controlling typhoon disaster hazards have become important research topics. The spatial–temporal characteristics of typhoons are analysed using the typhoon disaster data in Macau from 2000 to 2020. Computational fluid dynamics (CFD) numerical simulation is adopted to understand the 3D urban wind environment. Moreover, the 'exposure, sensitivity and adaptation' evaluation model is applied to construct the study framework. To calculate urban disaster vulnerability, the Create Fishnet tool is used to divide the city of Macau into 470 grids. The principal component analysis method is used to reveal the factors that significantly affect the typhoon's vulnerable areas. Result shows that 31.27% of grids are severely vulnerable. In addition, six principal components are identified, including indicators such as population density, building area ratio, mean elevation and wind speed. This study verifies the feasibility of wind speed data obtained by CFD in the typhoon evaluation model. Moreover, it provides a reliable reference guide for future urban microlevel studies. [ABSTRACT FROM AUTHOR]

Globally, urban waterbodies are continually degrading due to the stresses from both natural and man-made changes. The vulnerability of the water resources system is directly impacted by various threats other than population expansion, such as changes in land use, socio-economic development, and climate change and their sustainability is often challenging. Importance of the natural balance restoration becomes crucial, which would lead to a sustainable development. It is necessary to analyze the environmental deviations in a catchment and their amplitude and decide where one may contribute. This study focuses on assessing the pollution risk index of the urban waterbodies by developing an appropriate extension of WRASTIC (wastewater, recreation, agriculture, size of the watershed, transportation, industrial, and vegetation cover) namely, WRASTIC-HI (WRASTIC-Hazard Index), a novel multi-criteria analysis for identifying potential sources and degree of contamination. The vulnerability assessment of the Upper Lake of city Bhopal to contamination has been computed using WRASTIC-HI and the result indicated that the catchment is at high risk with a high score (71) which is entitled to a three-year waiver illustrating that if the threats continue to increase in and around the catchment, the lake will sooner face more risk. The proposed technique makes use of open-source data as well as field surveys, making it a highly helpful tool for evaluating ecosystems with little time and expense and supporting planners, managers, and administrators for sustainable planning and decision-making with no need for complex computations or the collection of exhaustive scientific data. [ABSTRACT FROM AUTHOR]

The year 2023 set summer temperature records, stressing the urgency of tackling greenhouse gas emissions and climate change, highlighting the need for a comprehensive approach that addresses vulnerable members of human society in urban, suburban, and rural environments. Vulnerability can be understood through the lens of equity, encompassing unjust social and political structures that dictate individuals' sensitivity and resilience to natural disasters. It also signifies an imbalance between structural factors, system dynamics, and the environment, resulting in varying susceptibility patterns across geographical scales. Addressing vulnerability's root causes involves examining structural factors, system dynamics, and the environment, alongside mapping heat vulnerability using diverse metrics for comparison and coherence. The intricate relationship between environmental hazards and human vulnerability underscores the need to comprehend the multifaceted concept of vulnerability. This involves understanding exposure, sensitivity, and adaptive capacity, collectively shaping an entity's susceptibility to extreme natural events. This review examines the intricate interplay between human vulnerability and environmental hazards, focusing on the context of heat-related risks. A more comprehensive understanding of heat challenges emerges by considering vulnerability variations influenced by human and environmental conditions. Population dynamics, often overlooked in vulnerability assessments, are recognized as critical determinants. We propose an integrated framework that advocates for incorporating changes in human and environmental conditions within vulnerability assessments, utilizing statistical predictive models to anticipate shifts due to population dynamics. Infrastructure and environmental factors are highlighted as essential components of vulnerability, requiring inclusion for accurate assessments at local levels. Challenges in vulnerability analysis, including defining outcomes and considering non-human species, are explored. Transformative heat policies are proposed to be concrete, inclusive, and responsive, emphasizing equity and involving stakeholders for effective governance. This review calls for more accurate, inclusive, and practical strategies for addressing heat-related vulnerabilities and enhancing community resilience. [ABSTRACT FROM AUTHOR]

Water and energy are mutually reinforcing elements in production and consumption activities, forming an inseparable relationship. This study, conducted at the provincial level, investigates the intricate connections between energy water footprint flow, water shortage, and water pollution. It aims to elucidate the vulnerability of provincial energy sectors to water use, fostering regional coordinated management for sustainable energy and water development. Utilizing a multi-regional input–output model, the research analyzes both the quantity and quality of water resources. The water footprint of China's provincial energy sector is quantified and scrutinized. Furthermore, an evaluation index system is constructed based on the water footprint concept. The entropy weight TOPSIS method is then applied to assess the vulnerability of inter-provincial energy sector water footprints. The key findings are as follows: (1) The energy water footprint has shifted from water resource-deficient areas, such as the northwest, North China, and northeast, to economically developed regions with relatively abundant water resources, such as East China and South China. This reveals that the transfer of water footprints in economic activities does not entirely alleviate water shortages in China and may even exacerbate shortages in certain water-deficient areas. (2) The graywater footprint of the energy sector is significantly larger than the blue water footprint and water shortage footprint. (3) The production and supply departments of power and heat emerge as the largest contributors to the transfer of energy and water footprints from water shortage areas to water-rich areas, followed by coal mining and dressing departments. (4) Top 10 Vulnerable Provinces: Tibet, Zhejiang, Ningxia, Tianjin, Jiangsu, Henan, Hunan, Beijing, Chongqing, and Hebei are identified as the top 10 provinces vulnerable to water footprints in the energy sector. This vulnerability closely correlates with the energy sector's dependence on water footprints and production leverage. Recommendations include optimizing the energy mix and establishing stable external water footprint supply channels for provinces with high water footprint dependence. Provinces with high production leverage are advised to accelerate innovation and application of water-saving technologies in key energy sectors, increase the utilization of clean energy, and enhance resource recycling. National government departments should focus on controlling and reducing water pollutants in the energy sector across regions, addressing water shortages by purifying water bodies and improving water quality. [ABSTRACT FROM AUTHOR]

Purpose: The 2008 Chinese melamine milk scandal resulted in six reported fatalities and affected around 300,000 children, of whom 54,000 were hospitalised. Previous studies have used linear approaches to examine the root causes of the melamine milk scandal. Design/methodology/approach: In the present study, we applied a systems approach to the melamine milk scandal to identify the complex systems-level failures across the supply chain leading to the incident and why food fraud incidents such as this occurred in the dairy sector. Additionally, systemic failures associated with food fraud vulnerability factors were considered (i.e. opportunities, motivation and control measures). Findings: 48 contributory factors of influence were identified and grouped across six sociotechnical levels across the Chinese dairy system, from government to equipment and surroundings. Lack of vertical integration (processes and communication) contributed to the failure. When viewed from a broader perspective, the melamine milk scandal can be linked to a series of human errors and organisational issues associated with government bodies, the dairy supply chain, individual organisations and management decisions and individual actions of staff or processes. Practical implications: This approach is of value to policymakers and the industry as it supports public health investigations of food fraud incidents and proactive food safety management. Originality/value: To the best of our knowledge, this is the first study to analyse a food safety or fraud incident using the AcciMap approach and the food fraud vulnerability assessment (FFVA) technique. AcciMap analysis is applied to both unintentional and intentional aspects of the incident. [ABSTRACT FROM AUTHOR]

The article presents a comprehensive framework for assessing the physical vulnerability of hand-dug wells within the Are Community, Southwestern Nigeria. The study spans from March to April 2023 and meticulously examines 90 wells, focusing on critical parameters such as well collar, well cover, and well lining information. The analysis reveals significant variations in well collar construction materials and dimensions, emphasizing the community’s adaptive strategies. The Well Collar Height Index (WCi), Well Cover Index (WCOi), Well Lining Index (WLi), and the derived Vulnerability Index categorize wells into vulnerability classes, offering a nuanced understanding of susceptibility levels. Notably, the study identifies wells with Very High vulnerability that demand urgent attention, as well as wells with effective protective measures categorized as Very Low vulnerability. The article emphasizes the need for a nuanced understanding of local practices and materials, highlighting the variability in well collar construction. It discusses the implications of well cover conditions and the critical role of well linings in assessing groundwater vulnerability. The Vulnerability Index combines these parameters, guiding targeted interventions based on risk severity. The study lays the groundwork for future interventions to enhance the safety and sustainability of water sources within the Are Community. It recommends immediate comprehensive measures for highly vulnerable wells, ongoing monitoring, community engagement, and knowledge sharing. The future scope includes incorporating geochemical analysis, targeted interventions, regular maintenance, community training, and exploring alternative water sources for sustainable improvements. [ABSTRACT FROM AUTHOR]

Indian agriculture transitioned from a food deficit sector to a food surplus following the Green Revolution. However, the continued progress of Indian agriculture has been hampered by climate change. This research explores the district-wise vulnerability in Madhya Pradesh, India, to climate change by assessing the composite vulnerability index using the agricultural vulnerability index (AVI) and socio-economic vulnerability index (SEVI). The study seeks to understand how agricultural and socio-economic factors lead to variations in vulnerability across districts and influence targeted adaptation and mitigation strategies. The trend analysis results present declining rainfall and inclining temperature from 1951 to 2021 in Madhya Pradesh, directly affecting the agricultural sector and human livelihood. The composite vulnerability index (CVI) results revealed that districts with low values (< 0.394), such as Burhanpur and Balaghat, demonstrate reduced susceptibility due to limited cultivation, low reliance on rainfall, lower drought susceptibility, and decreased population density. Districts such as Panna and Bhopal show moderate vulnerability (0.394–0.423), with lower fallow land, reduced rainfed agriculture, and socio-economic vulnerability. Extensive agriculture and marginalised workers' presence influence high vulnerability (0.423 to 0.456) in districts such as Tikamgarh and Indore. Districts like Barwani and Jhabua have the highest CVI values (> 0.456), indicating substantial susceptibility to climate impacts. The cluster analysis validates the results of the vulnerability index. The findings highlight the urgent need for tailored adaptation strategies to address the diverse agricultural and socio-economic indicators creating vulnerability in Madhya Pradesh. The study helps understand regional vulnerability patterns and provides evidence-based policy approaches for resilience to climate change effects. [ABSTRACT FROM AUTHOR]

The risk assessment of the area, with emphasis on the transport infrastructure, is dealt with in this paper. Disruption of some elements of transport infrastructure can lead to major negative impacts on the functioning of society, economies or states. The article therefore starts with presentation of the characteristics and importance of the road transport infrastructure with regard to its vulnerability. The core of the paper is the identification of vulnerable elements of road transport infrastructure and their parts and the subsequent vulnerability assessment of the selected element of the road transport infrastructure in the selected area. Therefore, the aim of the research, presented in this paper, was to propose a method for identifying and assessing the vulnerability of road transport infrastructure elements using the latest approaches in the field of vulnerability assessment. The proposed vulnerability assessment procedure is applied in the conditions of the city of Rajec in Slovakia.

A plethora of methods exist to aid decision-making in mitigating disaster risk, many of which fall into the categories of risk, vulnerability and/or resilience assessment methods. The objective of the present study is to provide an overview of these methods, with a particular focus on evidence that supports their practical implementation. A scoping study of scientific literature reveal 97 distinct methods. Despite the growing interest in publishing new methods, there seems to be a lack of focus on demonstrating their effectiveness in practice. The few contributions that do include some forms of evaluation typically do so by comparing the method's output with disaster data, by contrasting it with the output of other methods, by having experts evaluate the structure of the method, or by testing the method in practice and evaluating its usage. To further strengthen the relevance of research on this topic, increased attention should be paid to this matter. A good starting point would be to use the ways of evaluation identified in this study and systematically present evidence regarding the practical usefulness of methods for risk, vulnerability and/or resilience assessment.

Coastal areas are among the planet's most dynamic yet vulnerable environments, the shape of sand dunes being typically modeled by two sets of forces: the relatively constant action of wind and waves and the disturbances caused by infrequent but powerful storms. These meteorological events affect the plant communities, but can also play an important role in the long-distance dispersal of some psammophytes. Plants can be exposed, uprooted, broke apart and spread by wind and waves. Then, depending on the resilience of the plant to seawater temperature, the duration of immersion, the type of fragment transported and the suitability of the new habitat, new population can be established. This study aimed to assess the resilience to seawater immersion and the ability of long-distance dispersal of three psammophytes: Convolvulus persicus, Alyssum borzaeanum, and Silene thymifolia. The experiments tested the viability of vegetative fragments and the reproductive capacity of seeds under different exposure periods (5, 10, and 15 days) and temperature conditions: 4 °C (average surface seawater temperature during winter and early spring when storms on the Black Sea Coast occur), 13 °C (average surface seawater temperature), and 23 °C (average summer surface seawater temperature). The increase of both seawater temperature and the exposure time had a significant negative influence on the viability of the vegetative fragments and on germination capacity. After exposure to seawater, Convolvulus persicus, and Silene thymifolia are able to establish new populations through vegetative fragments or seeds, whilst in case of Alyssum borzaeanum, no vegetative regeneration was observed.